AUC provides an aggregate measure of performance across all possible
classification thresholds. One way of interpreting AUC is as the probability
that the model ranks a random positive example more highly than a random
negative example. For example, given the following examples, which are arranged
from left to right in ascending order of logistic regression predictions:
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